Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction.
Reprod Biol Endocrinol
; 22(1): 58, 2024 May 22.
Article
in En
| MEDLINE
| ID: mdl-38778410
ABSTRACT
BACKGROUND:
The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments.METHODS:
In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9.RESULTS:
Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p < 0.001). The Area Under the Curve (AUC) of iDAScore alone for predicting euploidy embryo is 0.612, but rose to 0.688 by adding clinical and embryonic characteristics.CONCLUSIONS:
This study provided additional information to strengthen the clinical applicability of iDAScore. This may provide a non-invasive and inexpensive alternative for patients who have no available blastocyst for biopsy or who are economically disadvantaged. However, the accuracy of embryo ploidy is still dependent on the results of next-generation sequencing technology (NGS) analysis.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Blastocyst
/
Preimplantation Diagnosis
/
Deep Learning
/
Aneuploidy
Limits:
Adult
/
Female
/
Humans
/
Pregnancy
Language:
En
Journal:
Reprod Biol Endocrinol
Journal subject:
ENDOCRINOLOGIA
/
MEDICINA REPRODUTIVA
Year:
2024
Type:
Article
Affiliation country:
China